An integral part of any successful business includes data management. The data may include anything from product data, service data, employee information, customer databases (including past, current and potential customers), accounting and other kinds of data. As the business grows, the data that needs to be managed may grow as well and may quickly become very large. A critical part of the data management process is filtering the large amount of data to obtain a specific or desired subset of data that may be of interest. For example, with a large amount of customer information, a company may desire to locate only a smaller subset of the customers for whom to market a particular product or service.
Conventional techniques of determining a set of parameters by which to filter the data to try and locate a subset of current interest has its own challenges. For example, filtering the data by too many parameters may yield a subset of data that is too small to be useful and conversely filtering the data by too few parameters may yield a subset of data that is larger than desired. Without greater visibility in the filtering process, extracting a desired subset of data from a larger set of data may be quite challenging.